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Qwen/Qwen3-235B-A22B-Instruct-2507

DeepInfra Inc./🇺🇸 US/chat

Qwen3-235B-A22B-Instruct-2507 is the updated version of the Qwen3-235B-A22B non-thinking mode, featuring Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.More
Compare 3 providersAll models →

Which id to call

deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507

This exact deployment on DeepInfra Inc., with no routing and no failover. Send it as the model field.

Input /1M

$0.07

DeepInfra Inc.

Output /1M

$0.10

1.4x input

Context

262K

tokens

Added

Jul 2025

chat

Capabilities 2/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What Qwen/Qwen3-235B-A22B-Instruct-2507 costs

Provider prices per 1M tokens, updated September 6, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

Input /1M

$0.07

Output /1M

$0.10

Cache write /1M

-

Cache read /1M

-

What a workload costs

100K input + 10K output
$0.0081
1M input + 100K output
$0.0810
10M input + 1M output
$0.81

At the rates above. This endpoint does not offer prompt caching, so repeated context is billed as new input every time.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507. Existing OpenAI SDK code needs no other edit.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window262K tokens
Max output-
API typechat
AddedJul 2025
Model id
Data retentionNone
Used for trainingNo
Served from🇺🇸 US
Privacy policyDeepInfra Privacy Policy →

Released 2025-07-25

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

Coding Indexcoding
22.1%

Artificial Analysis Coding Index: a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.

GPQA Diamondreasoning
79.0%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
19.9%

Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.

Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.

Same provider

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Newest first, on the same provider and the same key.

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1.0M ctx/$0.15/1M

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1.0M ctx/$1.20/1M

deepseek-v4-pro-0813

1.0M ctx/$1.30/1M

qwen3.8

262K ctx/$2.00/1M

deepseek-v4-flash-0731:flex

1.0M ctx/$0.07/1M

deepseek-v4-flash-0731

1.0M ctx/$0.09/1M

Reference

Qwen/Qwen3-235B-A22B-Instruct-2507 questions

How much does Qwen/Qwen3-235B-A22B-Instruct-2507 cost?

Qwen/Qwen3-235B-A22B-Instruct-2507 is priced at $0.07 per million input tokens and $0.10 per million output tokens when accessed via Requesty. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

What is the context window of Qwen/Qwen3-235B-A22B-Instruct-2507?

Qwen/Qwen3-235B-A22B-Instruct-2507 has a context window of 262K tokens. That's roughly 350 words of input you can fit in a single prompt.

How does Qwen/Qwen3-235B-A22B-Instruct-2507 perform on benchmarks?

Qwen/Qwen3-235B-A22B-Instruct-2507 scores 91.0% on Math Index, 91.0% on AIME 2025, 84.3% on MMLU Pro. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.

What can Qwen/Qwen3-235B-A22B-Instruct-2507 do?

Qwen/Qwen3-235B-A22B-Instruct-2507 supports tool calling, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use Qwen/Qwen3-235B-A22B-Instruct-2507 with the OpenAI SDK?

Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run Qwen/Qwen3-235B-A22B-Instruct-2507 through Requesty?

Yes. Qwen/Qwen3-235B-A22B-Instruct-2507 runs through Requesty's OpenAI-compatible API, served from DeepInfra Inc.. You do not host the model yourself: point base_url at Requesty, set the model to "deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call Qwen/Qwen3-235B-A22B-Instruct-2507 through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.

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